Yi vs llama
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""" PyTorch Yi model."""
# coding=utf-8
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch LLaMA model."""
import math
import math
import warnings
from typing import List, Optional, Tuple, Union
from typing import List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import torch.utils.checkpoint
from einops import repeat
from torch import nn
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
from ...activations import ACT2FN
BaseModelOutputWithPast,
from ...modeling_attn_mask_utils import AttentionMaskConverter, _prepare_4d_causal_attention_mask
CausalLMOutputWithPast,
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
SequenceClassifierOutputWithPast,
from ...modeling_utils import PreTrainedModel
)
from ...pytorch_utils import ALL_LAYERNORM_LAYERS
from transformers.modeling_utils import PreTrainedModel
from ...utils import (
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
from transformers.utils import (
add_start_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
add_start_docstrings_to_model_forward,
is_flash_attn_2_available,
logging,
logging,
replace_return_docstrings,
replace_return_docstrings,
)
)
from ...utils.import_utils import is_torch_fx_available
from .configuration_llama import LlamaConfig
from .configuration_yi import YiConfig
is_flash_attn_available = True
if is_flash_attn_2_available():
try:
from flash_attn import flash_attn_func, flash_attn_varlen_func
from flash_attn import flash_attn_func
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
except Exception:
is_flash_attn_available = False
# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
# It means that the function will not be traced through and simply appear as a node in the graph.
if is_torch_fx_available():
_prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
logger = logging.get_logger(__name__)
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "YiConfig"
_CONFIG_FOR_DOC = "LlamaConfig"
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
def _get_unpad_data(attention_mask):
def _make_causal_mask(
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
input_ids_shape: torch.Size,
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
dtype: torch.dtype,
max_seqlen_in_batch = seqlens_in_batch.max().item()
device: torch.device,
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
past_key_values_length: int = 0,
return (
):
indices,
"""
cu_seqlens,
Make causal mask used for bi-directional self-attention.
max_seqlen_in_batch,
"""
bsz, tgt_len = input_ids_shape
mask = torch.full(
(tgt_len, tgt_len),
torch.tensor(torch.finfo(dtype).min, device=device),
device=device,
)
mask_cond = torch.arange(mask.size(-1), device=device)
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat(
[
torch.zeros(
tgt_len, past_key_values_length, dtype=dtype, device=device
),
mask,
],
dim=-1,
)
return mask[None, None, :, :].expand(
bsz, 1, tgt_len, tgt_len + past_key_values_length
)
)
# Copied from transformers.models.bart.modeling_bart._expand_mask
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
warnings.warn(
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"Calling `transformers.models.llama.modeling_llama._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils.AttentionMaskConverter._prepare_4d_attention_mask"
"""
)
bsz, src_len = mask.size()
return AttentionMaskConverter._prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(
def _make_causal_mask(
inverted_mask.to(torch.bool), torch.finfo(dtype).min
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
):
warnings.warn(
"Calling `transformers.models.llama.modeling_llama._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.llama.modeling_llama.AttentionMaskConverter._make_causal_mask"
)
return AttentionMaskConverter._make_causal_mask(
input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
)
)
class YiRMSNorm(nn.Module):
class LlamaRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-5):
def __init__(self, hidden_size, eps=1e-6):
"""
"""
YiRMSNorm is equivalent to T5LayerNorm
LlamaRMSNorm is equivalent to T5LayerNorm
"""
"""
super().__init__()
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
self.variance_epsilon = eps
def forward(self, hidden_states):
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
return self.weight * hidden_states.to(input_dtype)
ALL_LAYERNORM_LAYERS.append(YiRMSNorm)
ALL_LAYERNORM_LAYERS.append(LlamaRMSNorm)
class YiRotaryEmbedding(torch.nn.Module):
class LlamaRotaryEmbedding(nn.Module):
def __init__(self, dim, max_position_embeddings=4096, base=5000000, device=None):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__()
super().__init__()
self.dim = dim
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.max_position_embeddings = max_position_embeddings
self.base = base
self.base = base
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
# Build here to make `torch.jit.trace` work.
# Build here to make `torch.jit.trace` work.
self._set_cos_sin_cache(seq_len=max_position_embeddings, device=device)
self._set_cos_sin_cache(
seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
)
def _set_cos_sin_cache(self, seq_len, device):
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
self.max_seq_len_cached = seq_len
inv_freq = 1.0 / (
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
)
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
freqs = torch.einsum("i,j->ij", t, inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer(
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
"cos_cached", emb.cos()[None, None, :, :], persistent=False
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
)
self.register_buffer(
"sin_cached", emb.sin()[None, None, :, :], persistent=False
)
def forward(self, x, seq_len=None):
def forward(self, x, seq_len=None):
# x: [bs, num_attention_heads, seq_len, head_size]
# x: [bs, num_attention_heads, seq_len, head_size]
if seq_len > self.max_seq_len_cached:
if seq_len > self.max_seq_len_cached:
self._set_cos_sin_cache(seq_len=seq_len, device=x.device)
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
return (
return (
self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
self.cos_cached[:seq_len].to(dtype=x.dtype),
self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
self.sin_cached[:seq_len].to(dtype=x.dtype),
)
)
class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
"""LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
self.scaling_factor = scaling_factor
super().__init__(dim, max_position_embeddings, base, device)
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
t = t / self.scaling_factor
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
"""LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
self.scaling_factor = scaling_factor
super().__init__(dim, max_position_embeddings, base, device)
def _set_cos_sin_cache(self, seq_len, device, dtype):
self.max_seq_len_cached = seq_len
if seq_len > self.max_position_embeddings:
base = self.base * (
(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
) ** (self.dim / (self.dim - 2))
inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
def rotate_half(x):
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, flash_attn_available):
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
# The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
"""Applies Rotary Position Embedding to the query and key tensors.
cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
Args:
expand_dim = 2 if flash_attn_available else 1
q (`torch.Tensor`): The query tensor.
cos = cos[position_ids].unsqueeze(expand_dim) # [bs, seq_len, dim]
k (`torch.Tensor`): The key tensor.
sin = sin[position_ids].unsqueeze(expand_dim) # [bs, seq_len, dim]
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`):
The position indices of the tokens corresponding to the query and key tensors. For example, this can be
used to pass offsetted position ids when working with a KV-cache.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
cos = cos[position_ids].unsqueeze(unsqueeze_dim)
sin = sin[position_ids].unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
return q_embed, k_embed
class YiMLP(nn.Module):
class LlamaMLP(nn.Module):
def __init__(self, hidden_size: int, intermediate_size: int, hidden_act: str):
def __init__(self, config):
super().__init__()
super().__init__()
self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
self.config = config
self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
self.hidden_size = config.hidden_size
self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
self.intermediate_size = config.intermediate_size
self.act_fn = ACT2FN[hidden_act]
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
self.act_fn = ACT2FN[config.hidden_act]
def forward(self, x):
def forward(self, x):
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
if self.config.pretraining_tp > 1:
slice = self.intermediate_size // self.config.pretraining_tp
gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
up_proj_slices = self.up_proj.weight.split(slice, dim=0)
down_proj_slices = self.down_proj.weight.split(slice, dim=1)
gate_proj = torch.cat(
[F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
)
up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
down_proj = [
F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
]
down_proj = sum(down_proj)
else:
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
return down_proj
class YiAttention(nn.Module):
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
class LlamaAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: YiConfig):
def __init__(self, config: LlamaConfig):
super().__init__()
super().__init__()
self.config = config
self.config = config
self.hidden_size = config.hidden_size
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.head_dim = self.hidden_size // self.num_heads
self.num_key_value_heads = config.num_key_value_heads
self.num_key_value_heads = config.num_key_value_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.max_position_embeddings = config.max_position_embeddings
self.max_position_embeddings = config.max_position_embeddings
self.rope_theta = config.rope_theta
self.is_causal = True
if (self.head_dim * self.num_heads) != self.hidden_size:
if (self.head_dim * self.num_heads) != self.hidden_size:
raise ValueError(
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {self.num_heads})."
f" and `num_heads`: {self.num_heads})."
)
)
self.q_proj = nn.Linear(
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
self.hidden_size, self.num_heads * self.head_dim, bias=False
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
self.k_proj = nn.Linear(
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
self._init_rope()
)
self.v_proj = nn.Linear(
self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
)
self.o_proj = nn.Linear(
self.num_heads * self.head_dim, self.hidden_size, bias=False
)
self.rotary_emb = YiRotaryEmbedding(
def _init_rope(self):
self.head_dim,
if self.config.rope_scaling is None:
max_position_embeddings=self.max_position_embeddings,
self.rotary_emb = LlamaRotaryEmbedding(
base=self.config.rope_theta,
self.head_dim,
)
max_position_embeddings=self.max_position_embeddings,
base=self.rope_theta,
)
else:
scaling_type = self.config.rope_scaling["type"]
scaling_factor = self.config.rope_scaling["factor"]
if scaling_type == "linear":
self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
scaling_factor=scaling_factor,
base=self.rope_theta,
)
elif scaling_type == "dynamic":
self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
scaling_factor=scaling_factor,
base=self.rope_theta,
)
else:
raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
def forward(
self,
self,
hidden_states: torch.Tensor,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
output_attentions: bool = False,
use_cache: bool = False,
use_cache: bool = False,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
if "padding_mask" in kwargs:
warnings.warn(
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
)
bsz, q_len, _ = hidden_states.size()
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states).view(
if self.config.pretraining_tp > 1:
bsz, q_len, self.num_heads, self.head_dim
key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
)
query_slices = self.q_proj.weight.split(
(self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
)
key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
key_states = self.k_proj(hidden_states).view(
query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
bsz, q_len, self.num_key_value_heads, self.head_dim
query_states = torch.cat(query_states, dim=-1)
)
value_states = self.v_proj(hidden_states).view(
bsz, q_len, self.num_key_value_heads, self.head_dim
)
if not is_flash_attn_available:
key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
if self.num_key_value_groups > 1:
key_states = torch.cat(key_states, dim=-1)
key_states = repeat(
key_states, f"b n h d -> b n (h {self.num_key_value_groups}) d"
)
value_states = repeat(
value_states, f"b n h d -> b n (h {self.num_key_value_groups}) d"
)
# b n h d -> b h n d
value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
query_states = query_states.transpose(1, 2)
value_states = torch.cat(value_states, dim=-1)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
seq_dim = 1 if is_flash_attn_available else 2
else:
kv_seq_len = key_states.shape[seq_dim]
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[seq_dim]
kv_seq_len += past_key_value[0].shape[-2]
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
query_states, key_states, cos, sin, position_ids, is_flash_attn_available
)
if past_key_value is not None:
if past_key_value is not None:
# reuse k, v, self_attention
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=seq_dim)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=seq_dim)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
past_key_value = (key_states, value_states) if use_cache else None
if is_flash_attn_available:
key_states = repeat_kv(key_states, self.num_key_value_groups)
attn_output = flash_attn_func(
value_states = repeat_kv(value_states, self.num_key_value_groups)
query_states, key_states, value_states, dropout_p=0.0, causal=True
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
raise ValueError(
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
f" {attn_weights.size()}"
)
)
else:
attn_weights = torch.matmul(
query_states, key_states.transpose(2, 3)
) / math.sqrt(self.head_dim)
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
raise ValueError(
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is"
f"{attention_mask.size()}"
)
attn_weights = attn_weights + attention_mask
dtype_min = torch.tensor(
torch.finfo(attn_weights.dtype).min,
device=attn_weights.device,
dtype=attn_weights.dtype,
)
)
attn_weights = torch.max(attn_weights, dtype_min)
attn_weights = attn_weights + attention_mask
# upcast attention to fp32
# upcast attention to fp32
attn_weights = nn.functional.softmax(
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_weights, dim=-1, dtype=torch.float32
attn_output = torch.matmul(attn_weights, value_states)
).to(query_states.dtype)
attn_output = torch.matmul(attn_weights, value_states)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
f" {attn_output.size()}"
)
)
if not is_flash_attn_available:
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
if self.config.pretraining_tp > 1:
attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
else:
attn_output = self.o_proj(attn_output)
if not output_attentions:
if not output_attentions:
attn_weights = None
attn_weights = None
return attn_output, attn_weights, past_key_value
return attn_output, attn_weights, past_key_value
class YiDecoderLayer(nn.Module):
class LlamaFlashAttention2(LlamaAttention):
def __init__(self, config: YiConfig):
"""
super().__init__()
Llama flash attention module. This module inherits from `LlamaAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
self.hidden_size = config.hidden_size
flash attention and deal with padding tokens in case the input contains any of them.
self.self_attn = YiAttention(config=config)
"""
self.mlp = YiMLP(
hidden_size=self.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
)
self.ln1 = YiRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.ln2 = YiRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
def forward(
def forward(
self,
self,
hidden_states: torch.Tensor,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
output_attentions: bool = False,
use_cache: Optional[bool] = False,
use_cache: bool = False,
) -> Tuple[
**kwargs,
torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
]:
# LlamaFlashAttention2 attention does not support output_attentions
"""
if "padding_mask" in kwargs:
Args:
warnings.warn(
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
)
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
"""
residual = hidden_states
hidden_states = self.ln1(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.ln2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
Yi_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`YiConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare Yi Model outputting raw hidden-states without any specific head on top.",
Yi_START_DOCSTRING,
)
class YiPreTrainedModel(PreTrainedModel):
config_class = YiConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["YiDecoderLayer"]
_skip_keys_device_placement = "past_key_values"
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, YiModel):
module.gradient_checkpointing = value
Yi_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
information on the default strategy.
- 1 indicates the head is **not masked**,
# overwrite attention_mask with padding_mask
- 0 indicates the head is **masked**.
attention_mask = kwargs.pop("padding_mask")
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
output_attentions = False
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
bsz, q_len, _ = hidden_states.size()
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
query_states = self.q_proj(hidden_states)
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
key_states = self.k_proj(hidden_states)
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
value_states = self.v_proj(hidden_states)
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
# Flash attention requires the input to have the shape
# batch_size x seq_length x head_dim x hidden_dim
# therefore we just need to keep the original shape
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
@add_start_docstrings(
kv_seq_len = key_states.shape[-2]
"The bare Yi Model outputting raw hidden-states without any specific head on top.",
if past_key_value is not None:
Yi_START_DOCSTRING,
kv_seq_len += past_key_value[0].shape[-2]
)
class YiModel(YiPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`YiDecoderLayer`]
Args:
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
config: YiConfig
"""
def __init__(self, config: YiConfig):
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(
if past_key_value is not None:
config.vocab_size, config.hidden_size, self.padding_idx
# reuse k, v, self_attention
)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
self.layers = nn.ModuleList(
value_states = torch.cat([past_key_value[1], value_states], dim=2)
[YiDecoderLayer(config) for _ in range(config.num_hidden_layers)]
)
self.norm = YiRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
past_key_value = (key_states, value_states) if use_cache else None
self.gradient_checkpointing = False
query_states = query_states.transpose(1, 2)
# Initialize weights and apply final processing
key_states = key_states.transpose(1, 2)
self.post_init()
value_states = value_states.transpose(1, 2)
def get_input_embeddings(self):
# TODO: llama does not have dropout in the config??
return self.embed_tokens
# It is recommended to use dropout with FA according to the docs
# when training.
dropout_rate = 0.0 # if not self.training else self.attn_dropout
def set_input_embeddings(self, value):
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
self.embed_tokens = value
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in the correct dtype just to be sure everything works as expected.
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
# in fp32. (LlamaRMSNorm handles it correctly)
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
input_dtype = query_states.dtype
def _prepare_decoder_attention_mask(
if input_dtype == torch.float32:
self, attention_mask, input_ids, inputs_embeds, past_key_values_length
# Handle the case where the model is quantized
):
if hasattr(self.config, "_pre_quantization_dtype"):
input_shape = input_ids.shape
target_dtype = self.config._pre_quantization_dtype
# create causal mask
else:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
target_dtype = self.q_proj.weight.dtype
combined_attention_mask = None
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(
input_shape,
inputs_embeds.dtype,
device=inputs_embeds.device,
past_key_values_length=past_key_values_length,
)
if attention_mask is not None:
logger.warning_once(
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
f"The input hidden states seems to be silently casted in float32, this might be related to"
expanded_attn_mask = _expand_mask(
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
f" {target_dtype}."
).to(inputs_embeds.device)
combined_attention_mask = (
expanded_attn_mask
if combined_attention_mask is None
else expanded_attn_mask + combined_attention_mask
)
)
return combined_attention_mask
query_states = query_states.to(target_dtype)
key_states = key_states.to(target_dtype)
@add_start_docstrings_to_model_forward(Yi_INPUTS_DOCSTRING)
value_states = value_states.to(target_dtype)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = (
attn_output = self._flash_attention_forward(
return_dict if return_dict is not None else self.config.use_return_dict
query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
)
)
# retrieve input_ids and inputs_embeds
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
if input_ids is not None and inputs_embeds is not None:
attn_output = self.o_proj(attn_output)
raise ValueError(
"You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
)
elif input_ids is not None:
batch_size, seq_length = input_ids.shape
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError(
"You have to specify either decoder_input_ids or decoder_inputs_embeds"
)
seq_length_with_past = seq_length
if not output_attentions:
past_key_values_length = 0
attn_weights = None
if past_key_values is not None:
return attn_output, attn_weights, past_key_value
past_key_values_length = past_key_values[0][0].shape[2]
seq_length_with_past = seq_length_with_past + past_key_values_length
if position_ids is None:
def _flash_attention_forward(
device = input_ids.device if input_ids is not None else inputs_embeds.device
self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
position_ids = torch.arange(
):
past_key_values_length,
"""
seq_length + past_key_values_length,
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
dtype=torch.long,
first unpad the input, then computes the attent